Information contained in the temporal patterns of neuronal activity is fundamental to neural processing. The origin of the temporal information is generally in the nature of the stimuli themselves, such as the temporal features of speech, but may also be neurally generated at early stages of sensory processing. How is temporal information decoded by the nervous system? What are the neural mechanisms that permit neurons to develop selective responses to temporal patterns on the time scale of tens to hundreds of milliseconds? The hypothesis guiding the current proposal is that local cortical networks are intrinsically capable of decoding temporal information. Specifically, that short-term plasticity produces time-dependent changes in the balance of opposing excitatory and inhibitory events, and that these time-dependent changes in network state allow neurons to respond differentially to the distinct temporal features of stimuli. Neuronal responses to time-varying stimuli are determined not only by the strength of their excitatory inputs, but by a balance of excitatory and inhibitory inputs, each of which is modulated by short-term forms of plasticity. The projects described here are aimed at understanding how multiple synaptic and cellular mechanisms interact, and the learning rules that govern the long-term plasticity of each process, including short-term plasticity itself. Towards this goal the Specific Aims of the current proposal will include: (1) Characterizing the interaction between short and long-term associative plasticity of excitatory synapses; (2) Determining whether the same protocols that induce plasticity of EPSPs, produce long-term plasticity of IPSPs and/or changes in presynaptically mediated forms of short-term plasticity; (3) Computational analysis of whether the orchestrated regulation of multiple synapse types in parallel - as observed in Aims 1 & 2 - may underlie the generation of interval selective neurons; (4) Experimental analysis of the dynamics of neocortical circuits and the ability of individual neurons embedded in these circuits to exhibit temporally selective responses. Together these studies should contribute to the understanding of how the brain times events, as well as to understanding cognitive deficits that may involve temporal processing (such as some forms of dyslexia), and to the generation of artificial systems capable of complex pattern recognition.